The charged particle beam inspection system has been widely used for the inspection of physical or electrical defects in a semiconductor wafer or mask. FIG. 1 is a schematic illustration of a conventional charged particle beam defect inspection system 100. System 100 includes several portions, namely, a primary charged particle beam source portion, a secondary charged particle detection portion, an image processing portion, and a system control portion.
The primary charged particle beam source portion includes a charged particle gun 10, a beam extraction electrode 11, a condenser lens 12, a beam blanking deflector 13, an aperture 14, a scanning deflector 15, and an objective lens 16. The secondary charged particle detection portion includes an E×B charged particle detour device 17, a secondary charged particle detector 21, a preamplifier 22, an A/D converter 23, and a high voltage electric source 24.
The image processing portion includes a first image storage 46, a second image storage 47, an arithmetic operation device 48 for comparing images from storage 46 and 47, a defect judgment device 49, and a monitor display 50 for interfacing with the users. The system control portion includes a microprocessor computer 6, a position correction and control circuit 43, a stage driver 34, an objective lens source 45, a scan signal generator 44, a sample stage 30, an X-Y direction stage 31, and a high voltage electric source 36.
The inspection process begins as a sample 9 (for example, a wafer or a mask) sitting on the sample stage 30 is irradiated by a primary charged particle beam 18. The secondary charged particles 20 emanating from the sample 9 is detoured by the E×B charged particle detour device 17 to the detector 21, the charged particle signal is then amplified by the amplifier 22 and converted to digital signals by the A/D converter 23 for further image processing and defect judgment. In accordance with a preloaded recipe in the firmware, the microprocessor computer 6 guides the stage driver 34 and scan signal generator 44 to properly deliver the inspection.
The throughput of the defect inspection process is particularly critical when such inspection is part of the semiconductor manufacturing process flow. It is usually necessary to be able to measure the defect density in a shorter time for high-efficiency production flow. Referring to FIG. 2, which is a flowchart illustration of a conventional defect inspection process 200. First, an imaging recipe is loaded, for example from the firmware, in step 210. The imaging recipe is a group of parameters carrying inspection specifications such as the graphical data system (GDS) file of the sample being inspected, the location and area of the sampling regions (regions on the sample that are interested in and to be inspected for defects), image pixel size, scan width, image scan time, the largest available image pixel size, the number of scans for individual sampling region, etc. It is noted that the sampling regions are generally selected in a fashion which complies with the statistical process control rule. Next, in step 220, a moving speed for the sample stage is calculated based on the loaded imaging recipe. This is especially important for a continuous scanning mode inspection operation. Then, the inspection is performed at the calculated stage speed and according to the specifications indicated in the imaging recipe, as shown in step 230. Then, in step 240, the density of inspected defects from step 230 is calculated. The calculation result is evaluated in step 250 to determine whether it meets satisfaction, so as to determine completion of the inspection. If the inspection is determined to be incomplete, the process goes back to step 230 and down. If the inspection is determined to be complete, the process ends at step 260.
The overall defect density inspection throughput typically scales as a function of the square of the pixel size used. For example, if the linear dimension of the image pixel size is halved in order to be able to find smaller defects, the overall inspection speed decreases by a factor of four assuming the pixel data rate (sampled pixels per unit time, for example in seconds) remains constant.
The square law dependence that throughput has on the image pixel size is critical for advanced higher resolution inspection systems such as a charged particle inspection system, where the frequent need to use smaller pixels slows the inspection speed significantly. The slow speeds of high resolution defect inspection, combined with the fact that minimum sample surface area must be inspected to make a statistically significant measurement of defect density, results in inefficient use of inspection system time.
This problem is especially significant when the sample comprises patterns composed of both interested and uninterested regions, or simply say that the sample comprises interested and uninterested regions. For example, when a functional device has been developed, it is typically desired to have immediate report of possible defects thereof. However, these devices often coexist with other common devices which are equally inspected in the conventional inspection method.
A similar problem may happen in a scenario where a newly developed fabrication method of an existing common device is to be verified by means of defect inspection. Further, it is also possible for a sample to have complete blank regions where no pattern is formed at all. In these cases, the inspection performed on those regions other than the interested region obviously lowers the overall throughput.
Referring to FIG. 3A, which illustrates an exemplary pattern having both interested and uninterested regions. The “interested regions” are substantially equal to the sampling regions in the conventional art mentioned earlier, and will be continued to be referred to as the sampling regions hereinafter for consistency. The uninterested region on the other hand, is a region which is not interested in and preferred to be skipped for the purpose of time saving, and will be referred as a skip region hereinafter. As shown, a target pattern 300A comprises an interested sampling region 310A and an uninterested skip region 320A. It is noted that the target pattern 300A in this example is a repeating pattern, and the sampling region 310A combined with the skip region 320A form a pattern period 330A.
FIG. 3B illustrates another exemplary pattern having both sampling regions and skip regions. As shown, an irregular target pattern 300B comprises a sampling region 310B and a skip region 320B, and no pattern period is present in this example. It is noted that in either case in FIGS. 3A and 3B, the uninterested skip region typically has a much larger area than the interested sampling region.
For example, the sampling region may be of a few hundreds nanometers (nm) in width, while the skip region may be of a few micrometers (um) in width. As a result, much of the tool time is wasted scanning the uninterested skip regions for the conventional art inspection method as in which both regions are equally scanned and imaged.
Accordingly what is needed is an inspection method that allows for increased inspection throughput without sacrificing resolution during inspection of the sample.